Wright State University, Dayton, Ohio, United States of America.
University of South Carolina, Columbia, South Carolina, United States of America.
PLoS One. 2021 Mar 25;16(3):e0248299. doi: 10.1371/journal.pone.0248299. eCollection 2021.
With the increasing legalization of medical and recreational use of cannabis, more research is needed to understand the association between depression and consumer behavior related to cannabis consumption. Big social media data has potential to provide deeper insights about these associations to public health analysts. In this interdisciplinary study, we demonstrate the value of incorporating domain-specific knowledge in the learning process to identify the relationships between cannabis use and depression. We develop an end-to-end knowledge infused deep learning framework (Gated-K-BERT) that leverages the pre-trained BERT language representation model and domain-specific declarative knowledge source (Drug Abuse Ontology) to jointly extract entities and their relationship using gated fusion sharing mechanism. Our model is further tailored to provide more focus to the entities mention in the sentence through entity-position aware attention layer, where ontology is used to locate the target entities position. Experimental results show that inclusion of the knowledge-aware attentive representation in association with BERT can extract the cannabis-depression relationship with better coverage in comparison to the state-of-the-art relation extractor.
随着医用和娱乐用大麻合法化的不断推进，我们需要开展更多研究，以了解抑郁与大麻消费行为之间的关联。大型社交媒体数据可能有助于公共卫生分析师更深入地了解这些关联。在这项跨学科研究中，我们展示了在学习过程中纳入特定领域知识的价值，以确定大麻使用与抑郁之间的关系。我们开发了一种端到端的知识融合深度学习框架（门控 K-BERT），该框架利用了预训练的 BERT 语言表示模型和特定领域的声明性知识源（药物滥用本体），通过门控融合共享机制共同提取实体及其关系。我们的模型进一步进行了定制，以通过实体位置感知注意力层为句子中提到的实体提供更多关注，其中本体用于定位目标实体的位置。实验结果表明，与最先进的关系提取器相比，在 BERT 中加入知识感知注意力表示可以更好地提取大麻与抑郁之间的关系。
